Estimation and Marginalization Using the Kikuchi Approximation Methods

2005 ◽  
Vol 17 (8) ◽  
pp. 1836-1873 ◽  
Author(s):  
Payam Pakzad ◽  
Venkat Anantharam
Keyword(s):  
Approximation Method ◽  
Message Passing ◽  
Belief Propagation ◽  
Iterative Algorithms ◽  
Product Distribution ◽  
Stationary Points ◽  
Minimal Graph ◽  
Minimal Graphs ◽  
Minimum Number ◽  
Free Case

In this letter, we examine a general method of approximation, known as the Kikuchi approximation method, for finding the marginals of a product distribution, as well as the corresponding partition function. The Kikuchi approximation method defines a certain constrained optimization problem, called the Kikuchi problem, and treats its stationary points as approximations to the desired marginals. We show how to associate a graph to any Kikuchi problem and describe a class of local message-passing algorithms along the edges of any such graph, which attempt to find the solutions to the problem. Implementation of these algorithms on graphs with fewer edges requires fewer operations in each iteration. We therefore characterize minimal graphs for a Kikuchi problem, which are those with the minimum number of edges. We show with empirical results that these simpler algorithms often offer significant savings in computational complexity, without suffering a loss in the convergence rate. We give conditions for the convexity of a given Kikuchi problem and the exactness of the approximations in terms of the loops of the minimal graph. More precisely, we show that if the minimal graph is cycle free, then the Kikuchi approximation method is exact, and the converse is also true generically. Together with the fact that in the cycle-free case, the iterative algorithms are equivalent to the well-known belief propagation algorithm, our results imply that, generically, the Kikuchi approximation method can be exact if and only if traditional junction tree methods could also solve the problem exactly.

scholarly journals Cooperative Localization for Mobile Networks: A Distributed Belief Propagation–Mean Field Message Passing Algorithm

2016 ◽  
Vol 23 (6) ◽  
pp. 828-832 ◽  
Author(s):  
Burak Cakmak ◽  
Daniel N. Urup ◽  
Florian Meyer ◽  
Troels Pedersen ◽  
Bernard H. Fleury ◽  
...  
Keyword(s):  
Mobile Networks ◽  
Message Passing ◽  
Belief Propagation ◽  
Mean Field ◽  
Cooperative Localization ◽  
Message Passing Algorithm

scholarly journals Constraint Satisfaction by Survey Propagation

2005 ◽  
Author(s):  
Alfredo Braunstein ◽  
Marc Mézard
Keyword(s):  
Constraint Satisfaction ◽  
Message Passing ◽  
Statistical Physics ◽  
Belief Propagation ◽  
Solution Space ◽  
Error Correcting Codes ◽  
Constraint Satisfaction Problems ◽  
Probabilistic Methods ◽  
Survey Propagation ◽  
Algorithmic Strategy

Methods and analyses from statistical physics are of use not only in studying the performance of algorithms, but also in developing efficient algorithms. Here, we consider survey propagation (SP), a new approach for solving typical instances of random constraint satisfaction problems. SP has proven successful in solving random k-satisfiability (k -SAT) and random graph q-coloring (q-COL) in the “hard SAT” region of parameter space [79, 395, 397, 412], relatively close to the SAT/UNSAT phase transition discussed in the previous chapter. In this chapter we discuss the SP equations, and suggest a theoretical framework for the method [429] that applies to a wide class of discrete constraint satisfaction problems. We propose a way of deriving the equations that sheds light on the capabilities of the algorithm, and illustrates the differences with other well-known iterative probabilistic methods. Our approach takes into account the clustered structure of the solution space described in chapter 3, and involves adding an additional “joker” value that variables can be assigned. Within clusters, a variable can be frozen to some value, meaning that the variable always takes the same value for all solutions (satisfying assignments) within the cluster. Alternatively, it can be unfrozen, meaning that it fluctuates from solution to solution within the cluster. As we will discuss, the SP equations manage to describe the fluctuations by assigning joker values to unfrozen variables. The overall algorithmic strategy is iterative and decomposable in two elementary steps. The first step is to evaluate the marginal probabilities of frozen variables using the SP message-passing procedure. The second step, or decimation step, is to use this information to fix the values of some variables and simplify the problem. The notion of message passing will be illustrated throughout the chapter by comparing it with a simpler procedure known as belief propagation (mentioned in ch. 3 in the context of error correcting codes) in which no assumptions are made about the structure of the solution space. The chapter is organized as follows. In section 2 we provide the general formalism, defining constraint satisfaction problems as well as the key concepts of factor graphs and cavities, using the concrete examples of satisfiability and graph coloring.

scholarly journals On Ramsey-Minimal Infinite Graphs

2021 ◽  
Vol 28 (1) ◽  
Author(s):  
Jordan Barrett ◽  
Valentino Vito
Keyword(s):  
Ramsey Theory ◽  
Infinite Graphs ◽  
Minimal Graph ◽  
Finite Graphs ◽  
Minimal Graphs ◽  
Finite Case ◽  
Ramsey Minimal Graph ◽  
General Conditions

For fixed finite graphs $G$, $H$, a common problem in Ramsey theory is to study graphs $F$ such that $F \to (G,H)$, i.e. every red-blue coloring of the edges of $F$ produces either a red $G$ or a blue $H$. We generalize this study to infinite graphs $G$, $H$; in particular, we want to determine if there is a minimal such $F$. This problem has strong connections to the study of self-embeddable graphs: infinite graphs which properly contain a copy of themselves. We prove some compactness results relating this problem to the finite case, then give some general conditions for a pair $(G,H)$ to have a Ramsey-minimal graph. We use these to prove, for example, that if $G=S_\infty$ is an infinite star and $H=nK_2$, $n \geqslant 1$ is a matching, then the pair $(S_\infty,nK_2)$ admits no Ramsey-minimal graphs.

scholarly journals Optimasi OPTIMASI BIAYA TRANSPORTASI PADA UKM DI KOTA BATAM

2021 ◽  
Vol 9 (1) ◽  
pp. 1-19
Author(s):  
Welly Sugianto ◽  
Elva Susanti
Keyword(s):  
Sensitivity Analysis ◽  
Objective Function ◽  
Approximation Method ◽  
Minimum Cost ◽  
Transportation Cost ◽  
Product Distribution ◽  
Mathematical Form ◽  
Total Production ◽  
Calculation Results ◽  
Iteration Methods

This research was conducted at UKM Jovelyn in Batam city. Jovelyn's UKM produces various kinds of cakes and is marketed in markets in Batam City. The UKM opened 4 branches and marketed its products to 7 markets in the city of Batam. Product distribution is still random and not properly regulated. This resulted in a very large transportation cost, up to 1/3 of the total production cost. This shows that product transportation is still not carried out effectively and efficiently. The transportation problem is converted into a mathematical form so that the problem can be solved by the transportation method. The transportation method aims to minimize the objective function which is a function of transportation costs. The transportation method is basically the same as the linear program where at each iteration a selection is made to enter the basic variabel and leave the basic variabel. There are several iteration methods, namely the northwest corner method, minimum cost method, genetic algorithm, Vogel's approximation method, minimum row method, Russell's approximation method and column minimum method. Previous research has shown that the Vogel's approximation method, and Russell's approximation method are more efficient and accurate. This study uses both methods and a sensitivity analysis is performed to optimize the calculation results. The sensitivity analysis aims to determine the extent to which the objective function constants and the constraint function constants can change Keywords: Transportation, Sensitivity, SME  

Locally-iterative Distributed (Δ + 1)-coloring and Applications

2022 ◽  
Vol 69 (1) ◽  
pp. 1-26
Author(s):  
Leonid Barenboim ◽  
Michael Elkin ◽  
Uri Goldenberg
Keyword(s):  
Message Passing ◽  
Edge Coloring ◽  
Iterative Algorithms ◽  
Independent Set ◽  
Local Model ◽  
Vertex Coloring ◽  
Maximal Independent Set ◽  
List Type ◽  
Inherent Limitation ◽  
Running Time

We consider graph coloring and related problems in the distributed message-passing model. Locally-iterative algorithms are especially important in this setting. These are algorithms in which each vertex decides about its next color only as a function of the current colors in its 1-hop-neighborhood . In STOC’93 Szegedy and Vishwanathan showed that any locally-iterative Δ + 1-coloring algorithm requires Ω (Δ log Δ + log * n ) rounds, unless there exists “a very special type of coloring that can be very efficiently reduced” [ 44 ]. No such special coloring has been found since then. This led researchers to believe that Szegedy-Vishwanathan barrier is an inherent limitation for locally-iterative algorithms and to explore other approaches to the coloring problem [ 2 , 3 , 19 , 32 ]. The latter gave rise to faster algorithms, but their heavy machinery that is of non-locally-iterative nature made them far less suitable to various settings. In this article, we obtain the aforementioned special type of coloring. Specifically, we devise a locally-iterative Δ + 1-coloring algorithm with running time O (Δ + log * n ), i.e., below Szegedy-Vishwanathan barrier. This demonstrates that this barrier is not an inherent limitation for locally-iterative algorithms. As a result, we also achieve significant improvements for dynamic, self-stabilizing, and bandwidth-restricted settings. This includes the following results: We obtain self-stabilizing distributed algorithms for Δ + 1-vertex-coloring, (2Δ - 1)-edge-coloring, maximal independent set, and maximal matching with O (Δ + log * n ) time. This significantly improves previously known results that have O(n) or larger running times [ 23 ]. We devise a (2Δ - 1)-edge-coloring algorithm in the CONGEST model with O (Δ + log * n ) time and O (Δ)-edge-coloring in the Bit-Round model with O (Δ + log n ) time. The factors of log * n and log n are unavoidable in the CONGEST and Bit-Round models, respectively. Previously known algorithms had superlinear dependency on Δ for (2Δ - 1)-edge-coloring in these models. We obtain an arbdefective coloring algorithm with running time O (√ Δ + log * n ). Such a coloring is not necessarily proper, but has certain helpful properties. We employ it to compute a proper (1 + ε)Δ-coloring within O (√ Δ + log * n ) time and Δ + 1-coloring within O (√ Δ log Δ log * Δ + log * n ) time. This improves the recent state-of-the-art bounds of Barenboim from PODC’15 [ 2 ] and Fraigniaud et al. from FOCS’16 [ 19 ] by polylogarithmic factors. Our algorithms are applicable to the SET-LOCAL model [ 25 ] (also known as the weak LOCAL model). In this model a relatively strong lower bound of Ω (Δ 1/3 ) is known for Δ + 1-coloring. However, most of the coloring algorithms do not work in this model. (In Reference [ 25 ] only Linial’s O (Δ 2 )-time algorithm and Kuhn-Wattenhofer O (Δ log Δ)-time algorithms are shown to work in it.) We obtain the first linear-in-Δ Δ + 1-coloring algorithms that work also in this model.

scholarly journals Analysis of TDMP Algorithm of LDPC Codes Based on Density Evolution and Gaussian Approximation

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 457 ◽  
Author(s):  
Xiumin Wang ◽  
Hong Chang ◽  
Jun Li ◽  
Weilin Cao ◽  
Liang Shan
Keyword(s):  
Message Passing ◽  
Belief Propagation ◽  
Gaussian Approximation ◽  
Bp Algorithm ◽  
Turbo Decoding ◽  
Density Evolution ◽  
Analysis Process ◽  
Evolution Analysis ◽  
Simplified Algorithm ◽  
Previous Iteration

Based on density evolution analysis of the existing belief propagation (BP) algorithm, the Turbo Decoding Message Passing (TDMP) algorithm was analyzed from the perspective of density evolution and Gaussian approximation, and the theoretical analysis process of TDMP algorithm was given. When calculating the prior message of each layer of the TDMP algorithm, the check message of the previous iteration should be subtracted. Therefore, the result will not be convergent, if the TDMP algorithm is directly analyzed based on density evolution and Gaussian approximation. We researched the TDMP algorithm based on the symmetry conditions to obtain the convergent result. When using density evolution (DE) and Gaussian approximation to analyze the decoding convergence of the TDMP algorithm, we can provide a theoretical basis for proving the superiority of the algorithm. Then, based on the DE theory, we calculated the probability density function (PDF) of the check-to-variable information of TDMP and its simplified algorithm, and then gave it a calculation based on the process of the normalization factor. Simulation results show that the decoding convergence speed of the TDMP algorithm was faster and the iterations were smaller compared to the BP algorithm under the same conditions.

Efficient nonparametric belief propagation for pose estimation and manipulation of articulated objects

2019 ◽  
Vol 4 (30) ◽  
pp. eaaw4523 ◽  
Author(s):  
Karthik Desingh ◽  
Shiyang Lu ◽  
Anthony Opipari ◽  
Odest Chadwicke Jenkins
Keyword(s):  
Pose Estimation ◽  
Message Passing ◽  
Belief Propagation ◽  
Sensor Data ◽  
Depth Sensor ◽  
Continuous Space ◽  
Articulated Objects ◽  
Message Passing Algorithm ◽  
Wide Range ◽  
Object Part

Robots working in human environments often encounter a wide range of articulated objects, such as tools, cabinets, and other jointed objects. Such articulated objects can take an infinite number of possible poses, as a point in a potentially high-dimensional continuous space. A robot must perceive this continuous pose to manipulate the object to a desired pose. This problem of perception and manipulation of articulated objects remains a challenge due to its high dimensionality and multimodal uncertainty. Here, we describe a factored approach to estimate the poses of articulated objects using an efficient approach to nonparametric belief propagation. We consider inputs as geometrical models with articulation constraints and observed RGBD (red, green, blue, and depth) sensor data. The described framework produces object-part pose beliefs iteratively. The problem is formulated as a pairwise Markov random field (MRF), where each hidden node (continuous pose variable) is an observed object-part’s pose and the edges denote the articulation constraints between the parts. We describe articulated pose estimation by a “pull” message passing algorithm for nonparametric belief propagation (PMPNBP) and evaluate its convergence properties over scenes with articulated objects. Robot experiments are provided to demonstrate the necessity of maintaining beliefs to perform goal-driven manipulation tasks.

scholarly journals An Executive Approach to Achieve Mutual Exclusion in Distributed Data using Topology and Association Rule.

2014 ◽  
Vol 13 (6) ◽  
pp. 4537-4542
Author(s):  
Mr. Anurag Singh ◽  
Dr. Amod Tiwari
Keyword(s):  
Distributed System ◽  
Association Rule ◽  
Message Passing ◽  
Computer Network ◽  
Mutual Exclusion ◽  
Distributed Data ◽  
Worst Case ◽  
New Approach ◽  
And Topology ◽  
Minimum Number

In this paper, a new approach is being proposed to achieve mutual exclusion in distributed system using computer network and topology of nth nodes. In this executive approach nodes communicate among themselves using message passing technique. In this executive approach, distributed system with n nodes is logically partitioned into number of sub distributed system having only m½ nodes, where m is obtained by adding a minimum number in n to make it next perfect square number only if n is not a perfect square. Proposed algorithm is a Token based approach and achieves token optimally in 2 messages only for the best case and in worst case a node achieves token in n messages only.

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